Personalized medicine is built on the notion that there is an inherent contradiction of going from studies of groups of patients to advice and recommendations for an individual patient. It involves tools and statistics to help clinicians advise one patient at a time, even in contradiction to results of group studies. A great part of these tools and statistics involve the genetic profile of the patient and other information (e.g., co-morbidity, concurrent medication, allergies), which can be used to tailor diagnoses and treatments based on patients' unique characteristics.
Yet, it is generally difficult to deduce from population/group studies what will work for an individual patient. Some medication may work for some patients, but not for others. A multitude of factors may account for any variation in medical effects. Examples include the type of medication, dosage, absorption rate, severity of illness, drug-drug interactions, allelic combination of a patient's genes encoding detoxification enzymes, age, nutritional status, and co-morbidities. Given the complexity of determining the right medication for patients, health providers need a tool for providing more effective prescriptions beyond the trial and error methods.